Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the annotations (readOnly=false, idempotent=false), the description adds real context: the campaign is only a draft at this stage, requires admin-enabled SMS and a configured provider, and carries a ₦2/contact cost for curated lists with budget required on the platform route. This meaningfully extends what annotations alone convey, though it stops short of describing return values or failure behaviors.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact (~85 words), front-loaded with the core action, then structured as audience options → workflow → prerequisites. Every sentence earns its place, though the density of the sourceType enumeration makes it slightly harder to parse at a glance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 10-parameter tool with no output schema, the description covers the critical decision (sourceType), the follow-up workflow, and prerequisites, while the schema covers remaining parameter details at 100%. The main gap is missing return-value disclosure (e.g., whether a campaign ID is returned), which matters because no output schema exists to fill that void.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, and the description earns an extra point by adding plain-language semantics to sourceType — explaining each enum value as an audience route (own contacts, raw numbers, wider platform audience, opted-in marketplace lists). Other parameters are already well-documented in the schema, so the description wisely focuses its added value on the most complex decision.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific action and resource — 'Create a draft SMS campaign' — with the word 'draft' clearly distinguishing it from the send tool. The description also names the complementary tools (neuron_estimate_sms_campaign, neuron_send_sms_campaign), making the tool's role in the SMS lifecycle unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit workflow sequencing: create the draft first, then estimate pricing, then send. It also routes audience selection by explaining each sourceType option in plain language. However, it never states when NOT to use this tool versus alternative campaign-creation siblings (e.g., neuron_create_broadcast or neuron_create_campaign), leaving exclusion logic implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.